Data Mining and Knowledge Engineering

M. Sreekrishna ¹, B. Chitra ¹, A. Naveen Kumar ¹

Affiliations
1 Angel College of Engineering and Technology, Tirupur, IN

Abstract

The deep web are the web that are not a part of surface web. Due to the large volume of data deep web have grained a large attention in recent years. Traditional search engines cannot be used to retrieve content in the deep Web. Those pages do not exist until they are created dynamically as the result of a specific search. The deep web is found to be large magnitude than the surface web. Further those deep web mostly comprises of online domain specific databases, which are accessed by using web query interfaces. In order to make the extraction relevant to user it is necessary to classify the deep web database. In this paper unclassified ontology based web classification method is used for to classify the data in the deep web. This method involves completely unclassified set of data and uses Wikipedia category network for to analyze the meta-information of the deep web sources. The result of the experiment is found to more accurate and fine-grained classification when compared to the existing approaches.

Keywords

Deep Web, Ontology, Semantic Information Retrieval, Semantic Search, Wikipedia.

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B. Kavitha ¹, D. Shanmuga Priyaa ¹, B. Chitra ²

Affiliations
1 Department of Computer Science, Karpagam University, Coimbatore-21, IN
2 Department of Computer Applications, Shri Nehru Maha Vidyalaya College of Arts and Science, Coimbatore-21, IN

Abstract

Data Mining is the process of extracting patterns from data. Machine Learning is a scientific discipline that is concerned with the design and development of algorithms that allow computers to learn based on data, such as from sensor data or databases. A major focus of machine learning research is automatically learn to recognize complex patterns and make intelligent decisions based on data. Engineers and researchers in the automobile industry have tried to design and build safer automobiles, but traffic accidents are unavoidable. Patterns involved in dangerous crashes could be detected if we develop a prediction model that automatically classifies the type of injury severity of various traffic accidents. These behavioral and roadway patterns are useful in the development of traffic safety control policy. We believe that to obtain the greatest possible accident reduction effects with limited budgetary resources, it is important that measures be based on scientific and objective surveys of the causes of accidents and severity of injuries. This paper deals about some classification models to predict the severity of injury that occurred during traffic accidents using two machine-learning approaches. We compared Naive Bayesian classifier and J48 decision tree Classifier for classifying the type of injury severity of various traffic accidents and the result shows that J48 outperforms Naive Bayesian.

Keywords

Data Mining, J48 Decision Tree Classifier, Machine Learning, Naive Bayesian Classifier, Prediction.

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